Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing
| Autor: | Mihaela van der Schaar, Sabrina Muller, Cem Tekin, Anja Klein |
|---|---|
| Přispěvatelé: | Tekin, Cem |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Networks and Communications Computer science media_common.quotation_subject Computer Science - Human-Computer Interaction 02 engineering and technology Crowdsourcing Machine learning computer.software_genre Machine Learning (cs.LG) Human-Computer Interaction (cs.HC) Task (project management) 020204 information systems 0202 electrical engineering electronic engineering information engineering Overhead (computing) Quality (business) Electrical and Electronic Engineering media_common Social and Information Networks (cs.SI) business.industry Context (computing) 020206 networking & telecommunications Task assignment Computer Science - Social and Information Networks Maximization Computer Science Applications Online learning 8. Economic growth Task analysis Artificial intelligence business Mobile device computer Software Contextual multi-armed bandits |
| Zdroj: | IEEE/ACM Transactions on Networking |
| Popis: | In mobile crowdsourcing (MCS), mobile users accomplish outsourced human intelligence tasks. MCS requires an appropriate task assignment strategy, since different workers may have different performance in terms of acceptance rate and quality. Task assignment is challenging, since a worker's performance (i) may fluctuate, depending on both the worker's current personal context and the task context, (ii) is not known a priori, but has to be learned over time. Moreover, learning context-specific worker performance requires access to context information, which may not be available at a central entity due to communication overhead or privacy concerns. Additionally, evaluating worker performance might require costly quality assessments. In this paper, we propose a context-aware hierarchical online learning algorithm addressing the problem of performance maximization in MCS. In our algorithm, a local controller (LC) in the mobile device of a worker regularly observes the worker's context, her/his decisions to accept or decline tasks and the quality in completing tasks. Based on these observations, the LC regularly estimates the worker's context-specific performance. The mobile crowdsourcing platform (MCSP) then selects workers based on performance estimates received from the LCs. This hierarchical approach enables the LCs to learn context-specific worker performance and it enables the MCSP to select suitable workers. In addition, our algorithm preserves worker context locally, and it keeps the number of required quality assessments low. We prove that our algorithm converges to the optimal task assignment strategy. Moreover, the algorithm outperforms simpler task assignment strategies in experiments based on synthetic and real data. 18 pages, 10 figures |
| Databáze: | OpenAIRE |
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